First, for the online scenario where multiple jobs (DAGs) coexist,
we consider how to schedule these jobs with the same weights. In this
case, the optimization objective is equivalent to minimizing the average
job completion time (JCT). We build a job DAG generator to generate
Poisson flow jobs; the time interval of job arrival obeys an exponential
distribution. Unless otherwise specified, we choose the number of
ports \(P\)  = 20, the average job arrival rate \(\lambda \)  = 20 and
the average number of coflows \(n\)  = 8. In the experiment, we compare
our algorithm with DeepWeave, both based on DRL and Varys [1]}
and IAOA [2]} that were also used as popular non-ML
baselines for comparison with DeepWeave [3]}. In addition,
we add an ablation experiment to compare the performance of the retrained
model under the same settings, removing the self-attention layer.
The results of the average JCT is shown in Fig. REF .
Compared with DeepWeave, the average JCT of our algorithm is reduced
by up to 40.42%, and the completed jobs is at least 1.68 times more.
Besides, in the case of removing the self-attention layer, the performance
of the model loses 29.36%.
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